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A Weekly Load Data Mining Approach Based on Hidden Markov Model

Shixiang Lu, Guoying Lin, Hanlin Liu, Chengjin Ye, Huakun Que, Yi Ding
2019 IEEE Access  
INDEX TERMS Weekly load profiles, dimension reduction, clustering, hidden Markov model evaluation.  ...  A feasible tool based on dynamic characteristics of load patterns is invented to evaluate the short-term load forecasting methods, which realizes the pre-check for the forecasting results without future  ...  For a short-term load forecasting problem, various forecasting methods and models are available.  ... 
doi:10.1109/access.2019.2901197 fatcat:yi7iapdegvbk5k6ho4vvbixsam

Data-Driven Load Modeling and Forecasting of Residential Appliances [article]

Yuting Ji, Elizabeth Buechler, Ram Rajagopal
2018 arXiv   pre-print
This paper proposes a conditional hidden semi-Markov model to describe the probabilistic nature of residential appliance demand, and an algorithm for short-term load forecasting.  ...  The expansion of residential demand response programs and increased deployment of controllable loads will require accurate appliance-level load modeling and forecasting.  ...  SHORT-TERM LOAD FORECASTING In this section, we describe an algorithm for short-term load forecasting using the proposed CHSMM.  ... 
arXiv:1810.03727v1 fatcat:x3tbblnhwvaungghjpnzckyvaa

Forecasting real-time locational marginal price: A state space approach

Yuting Ji, Jinsub Kim, Robert J. Thomas, Lang Tong
2013 2013 Asilomar Conference on Signals, Systems and Computers  
Such a short-term forecast provides actionable information for market participants and system operators.  ...  By incorporating real-time measurements and forecasts, the proposed forecasting algorithm generates the posterior probability distribution of future locational marginal prices with forecast horizons of  ...  With the concept of LMP states, modeling the real-time LMP state process (S t ) as a discrete Markov chain becomes natural.  ... 
doi:10.1109/acssc.2013.6810300 dblp:conf/acssc/JiKTT13 fatcat:eu4kl54lzvdhnbh2a5rjtjg4jm

Enhanced Markov-Difference Based Power Consumption Prediction for Smart Grids

Yiwen Le, Jinghan He
2017 Journal of Electrical Engineering and Technology  
Experimental results validate that the proposed SMMD achieves an improvement in terms of the prediction accuracy with respect to state-of-the-art solutions.  ...  With careful investigation of practical power datasets, we find an interesting phenomenon that the stochastic property of practical power datasets does not follow the Markov features.  ...  Therefore, RAF method is a high effective and practical method for short-term forecasts [22] .  ... 
doi:10.5370/jeet.2017.12.3.1053 fatcat:axhbpzgzuzelrmadufsbvcqvpm

Short-Term Industrial Load Forecasting Based on Ensemble Hidden Markov Model

Yuanyuan Wang, Yang Kong, Xiafei Tang, Xiaoqiao Chen, Yao Xu, Jun Chen, Shanfeng Sun, Yongsheng Guo, Yuhao Chen
2020 IEEE Access  
INTRODUCTION Short-term load forecasting (STLF) is a key technology for smart grid [1] .  ...  HIDDEN MARKOV MODEL The HMM is a doubly stochastic model, in which the probability of the load value is conditioned on a small number of discrete ("hidden") states representing the customer's random behavior  ... 
doi:10.1109/access.2020.3020799 fatcat:unzeg7dqmrgcpnhnwynvwbqm54

Short-Term Electric Load Prediction and Early Warning in Industrial Parks Based on Neural Network

Guannan Wang, Pei Yang, Jiayi Chen, Daqing Gong
2021 Discrete Dynamics in Nature and Society  
extraction, and applies the attention-based model for load forecasting.  ...  This paper proposes a load forecasting method based on LSTM model, fully explores the regularity of historical load data of industrial park enterprises, inputs the data features into LSTM units for feature  ...  With the continuous development of deep learning and the strengthening of computer computing power, short-term power load forecasting based on deep learning has gradually become the state of the art.  ... 
doi:10.1155/2021/1435334 fatcat:ntlnvgpaxbgfxoukztqxuyjmmm

Information in (and not in) the Term Structure

Gregory R. Duffee
2011 The Review of financial studies  
Kalman filter estimation uncovers a factor that has an almost imperceptible effect on yields, but has clear forecast power for future short-term interest rates and substantial forecast power for future  ...  However, this is not required by finance theory, nor is it consistent with observed Treasury yield behavior.  ...  The standard Gaussian model I use a standard discrete time Gaussian term structure framework. The use of discrete time is innocuous.  ... 
doi:10.1093/rfs/hhr033 fatcat:nctxwwldgjcgbcpevkheqzk2rq

A Hybrid Dynamic Programming and Neural Network Approach to Unit Commitment with High Renewable Penetration

Sahar Kaddah, K. Abo-Al-Ez, Tamer Megahed, M. Osman
2020 Bulletin of the Faculty of Engineering. Mansoura University  
Forecasting model was built by using a hybrid Markov to forecast solar radiation, while, autoregressive integrated moving average model is used to predict wind speed.  ...  To ensure economical with the stochastic nature of renewable sources, it is essential to develop an efficient forecasting model for renewable power generation.  ...  Power Forecasting There are three time horizons for wind speed forecasting, very short term, short term and medium term forecasting.  ... 
doi:10.21608/bfemu.2020.99332 fatcat:nguarf2fgnhj5hvw6jl2cr52xe

Power Load Forecasting using Back Propagation Algorithm

Load forecasting is categorized into short, medium and long term forecasts. The short term prediction refers to hourly load forecast for the period lasting from an hour to more than a few days.  ...  Excellence of short term hourly power load forecast has a considerable effect on cost effective functioning of power plants because numerous assessments are dependent on these forecasts.  ...  There is another term called prediction which appears to be similar to forecasting with respect to definition.  ... 
doi:10.35940/ijitee.a1031.0881019 fatcat:klnsk5r2a5fvbizmpzntshhr54

Machine Learning-Based Short-Term Prediction of Air-Conditioning Load through Smart Meter Analytics

2017 Energies  
The present paper is focused on short-term prediction of air-conditioning (AC) load of residential buildings using the data obtained from a conventional smart meter.  ...  The main advantage of the present methodology is separating the AC consumption from the consumptions of other residential appliances, which can then be predicted employing short-term weather forecasts.  ...  Figure 1 . 1 Hidden Markov Model [31] . Figure 1 . 1 Hidden Markov Model [31] . Figure 2 . 2 Factorial Hidden Markov Model [31] . Figure 2 . 2 Factorial Hidden Markov Model [31] .  ... 
doi:10.3390/en10111905 fatcat:hnxcqvggq5g6npbzidksmtdppm

Pre-Processing of Energy Demand Disaggregation Based Data Mining Techniques for Household Load Demand Forecasting

Ahmed F. Ebrahim, Osama A. Mohammed
2018 Inventions  
These proposed algorithms include two benchmark disaggregation algorithms; Factorial Hidden Markov Model (FHMM), Combinatorial Optimization in addition to three adopted Deep Neural Network; long short-term  ...  This paper introduces an innovative methodology to enhance household demand forecasting based on energy disaggregation for Short Term Load Forecasting.  ...  The state transitions of devices are controlled by the hidden Markov model (HMMs) which is a statistical tool.  ... 
doi:10.3390/inventions3030045 fatcat:no2gw5nkgbdi5isquuto45vinm

Modeling Variability and Uncertainty of Photovoltaic Generation: A Hidden State Spatial Statistical Approach

Michaelangelo D. Tabone, Duncan S. Callaway
2015 IEEE Transactions on Power Systems  
In this paper, we construct, fit, and validate a hidden Markov model for predicting variability and uncertainty in generation from distributed (PV) systems.  ...  We find that PV variability distributions are roughly Gaussian after conditioning on hidden states.  ...  Hidden Markov models have also been used in the prior literature to forecast mean clearness index of PV insolation [18] - [20] .  ... 
doi:10.1109/tpwrs.2014.2372751 fatcat:mx3qj5ntw5f6jayblq5tjyzwr4

Smart Home Management: State-Space Approximate Dynamic Programming

Zhiheng Zhao, Chanaka Keerthisinghe
2020 IEEE Access  
INDEX TERMS PV-storage systems, smart home energy management, state-space approximate dynamic programming, machine learning, long short-term memory recurrent neural networks VOLUME 4, 2020  ...  This paper proposes a novel state-space approximate dynamic programming (SS-ADP) approach to quickly solve a SHEMS problem but with similar solutions as DP.  ...  Figure 3 : 3 Long Short Term Memory FIGURE 9 . 9 Long short-term memory cell.  ... 
doi:10.1109/access.2020.3023665 fatcat:edszyaedbfeknindft46frvl6m

Improving A Grid-Based Energy Efficiency by Using Service Sharing Strategies

Boonyong Punantapong, Panit Punantapong, Itthidech Punantapong
2015 Energy Procedia  
Therefore, it has been used here is useful in all kind of power forecasting such as short term, middle term, and long term.  ...  It is tried to find out the pattern of electrical power usage with the dataset which is prepared by real data.  ...  Short term load forecasting: The load forecasting for 1 day to 1 week. Medium term load forecasting: The load forecasting from several weeks to one or several years.  ... 
doi:10.1016/j.egypro.2015.11.586 fatcat:gr67v7wnhvcjhnmyw4vqzc4fhm

Towards Modified Entropy Mutual Information Feature Selection to Forecast Medium-Term Load Using a Deep Learning Model in Smart Homes

Omaji Samuel, Fahad A. Alzahrani, Raja Jalees Ul Hussen Khan, Hassan Farooq, Muhammad Shafiq, Muhammad Khalil Afzal, Nadeem Javaid
2020 Entropy  
For accurate load forecasting, this paper proposes a model for medium-term load forecasting that uses hourly electrical load and temperature data to predict month ahead hourly electrical loads.  ...  Among the several load forecasting methods, medium-term load forecasting is necessary for grid's maintenance planning, settings of electricity prices, and harmonizing energy sharing arrangement.  ...  load forecasting Very short-term load forecasting WLSSVM Wavelet least square support vector machine immune system AR-MTLF An accurate and robust medium-term load forecasting CRBM Condition restricted  ... 
doi:10.3390/e22010068 pmid:33285843 fatcat:rboggykthjgoxj7erv6rwql2we
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